Combining C4.5 Recommendations

Up to this point, all experiments have shown the results of EUREKA
selecting a single strategy, all other strategy results being fixed.
In this experiment we allow EUREKA to select all strategy choices
at once for a given problem and execute the parallel search with the recommended
strategies. We then compare
the results to each fixed strategy (the fixed strategy choice is averaged
over all problem instances and all possible choices of other strategy
decisions). A random
set of 50 problems from the fifteen puzzle domain is selected and run
on 64 processors of the nCUBE 2. Table 13
summarizes the speedup for each approach.

Table 13:
Combination of C4.5 Recommendations

Approach

Speedup

Eureka

74.24

Random Processor LB

70.75

Local Ordering

68.92

Transformation Ordering

66.13

Kumar and Rao

65.89

Distributed Tree

65.82

Fixed Evaluation 1

65.41

1 Cluster

65.21

Neighbor LB

65.21

30% Distribution

65.21

2 Clusters

64.97

50% Distribution

61.94

Fixed Evaluation 2

49.58

4 Clusters

49.57

Avg. of Fixed Strategies

63.43

These results indicate that EUREKA can effectively make all strategy
choices at once. The learned rules achieve better performance than that
obtained by any one of these strategy choices.
These rules also outperform any single fixed strategy choice
averaged over all other parameter options.